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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

Showing 111120 of 691 papers

TitleStatusHype
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language ModelsCode1
MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic InsightsCode1
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
Category-Specific Topological Learning of Metal-Organic Frameworks0
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations0
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
PepMNet: a hybrid deep learning model for predicting peptide properties using hierarchical graph representationsCode1
M^3-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and DiscoveryCode2
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
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